How Machine Learning Can Improve Fleet Safety

The transportation safety industry is rapidly transforming. As fleets are becoming more data-driven and digital, they are challenged to make good decisions quickly, while still providing a competitive advantage through innovation and quality-of-service. Fleets know that making these decisions require the right data at the right time.

Compounding the issue of getting accurate fleet data, the shear amount of driver information is ever increasing. Year-over-year more sophisticated 3rd party systems get added to your fleet and safety operations such as telematics, HR systems, cameras, sleep apnea programs, training, and drug and alcohol programs. Gone are the days when the issue was not enough driver data is available. Now, in large part, the issue is there is too much data to ingest.

To assist with this influx of data, some fleets are getting rid of the complex spreadsheets and disparate third-party systems. They are managing their safety and compliance teams by using centralized data management platforms. With a holistic view of the data, the problem can begin to be addressed. You can get a complete view of your fleet but also have the ability to dive deep into specific driver tendencies. But this only solves part of the equation. Enter… machine learning.

What is Machine Learning?

Machine learning is based on the idea that you can give machines access to data and let them learn for themselves. This system provides the ability to ingest massive amounts of data, or features, to build models around patterns of behaviors. Using these features, the model can automatically learn and improve by adapting to provide outputs of known events from experience without being explicitly programmed. As the model evolves and improves, it can then be fed new and live data to predict those same outcomes for events that have not yet happened. This is the predictive power in at play.

The real magic behind this technology and these techniques is assigning weights to the features and automatically determining which features are significant predictors. With the potential of thousands of inputs and features to make up the model, it’s impossible and incredibly inefficient to try and work through the best combinations and weights by hand. These weights, or how important each feature is, are what the machine actually learns as it runs hundreds of thousands of simulations, trying to match the best combinations with actual known outcomes.

How Can It Improve Fleet Safety?

A practical and very powerful example of machine learning in trucking is predicting at-risk drivers and potential accidents. If you have a rich and complete set of data across some tens of thousands of drivers and a few decades, you can start to use machine learning techniques to build a predictive model. By segmenting the data, you can take all the known accidents or incidents after a certain point in time and look back at the data or behaviors leading up to those accidents

In feeding this pre-accident data into a model, you can run it through a cycle and see which drivers it would have predicted and compare that to the actual outcomes to see your accuracy. Now the machine slightly tweaks the weights of the inputs in the model and runs it again. If the accuracy improved, it knows its on the right track. If it digresses, it starts to see why. It then tweaks the weights again and runs another cycle. The machine goes through this process hundreds of thousands of times, ‘learning’ as it goes, until it finally settles on the optimal weights for the optimal model that you’ve given it.

The more driver and accident data you have in one place, the better the outcome will be. It’s important to note, though, that not only is rich and comprehensive data necessary, but an understanding of the industry and the myriad of different models you can use to start with. Each different model or technique is used for a different application of machine learning or for a different type of data flow. Understanding, choosing, and implementing the right one is critical to a successful and accurate prediction. When applied to trucking safety, this accuracy in prediction directly translates to lives saved and accidents prevented.

How Will This Impact Operations?

With the right implementation of a machine learning technology, you can mitigate risks, prevent accidents and reduce insurance claims. When combined with the centralized data management software, a machine learning analytics tool, can enable you to gather information faster, make predictions and manage fleet exceptions.

The successful fleets of the 21st century will be the visionaries who understand how to use their driver data. By uncovering new insights hidden within complex fleet data, fleets can positively impact all drivers out on the road. Machine learning can not only help fleets save time, money, and the lives of their drivers, but also help make roads and highways safer for everyone.